Sateesh Gudla 3 1 ( January - March ) Machine learning systems in the brain signal processing field of electroencephalography EEG have established a good performance in neural signal analysis emotion recognition systems seizure detection and braincomputer interface BCI One major difficulty that needs to be addressed in realworld applications is that models derived from one EEG dataset will generally suffer a large drop in performance if tested on a different one as a result of differences in equipment participating in the recordings electrode placement subject and demographic differences environmental noise and individual subject variability This paper introduces a new hybrid crossdataset quantum domain adaptation QDA framework that combines covariancebased feature extraction manifold projection adversarial maximum mean discrepancy MMD domain alignment and parametric quantum kernel learning with parameterized quantum circuits The experimental results validate that the proposed framework can attain 978 classification accuracy which exceeds the accuracy of conventional CNN 892 CNNQSVM 924 and Transformer 946 models It also enhances accuracy across all the metrics like precision recall and F1score Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026